{"title":"Modified mesh-connected computers for image processing applications","authors":"M. Hamdi","doi":"10.1109/AISPAS.1995.401344","DOIUrl":null,"url":null,"abstract":"The paper proposes a new architecture, which has the potential to support low-level image processing as well as intermediate and high-level vision analysis efficiently. The integrated architecture consists of a mesh of processors enhanced with an efficient recursive network. Low-level image processing is performed on the mesh processor, while intermediate and high-level vision analysis is performed on the recursive network. The interaction between the two levels is supported by parallel data and control paths. To illustrate the power of such a two-level system, we present efficient parallel algorithms for a variety of problems from low-level image processing to high-level vision. Representative applications include image component labeling convexity problems, and histogram-type operations. Through time complexity analysis, we show that the integrated architecture meets the requirements of many image understanding tasks.<<ETX>>","PeriodicalId":321580,"journal":{"name":"Proceedings the First Aizu International Symposium on Parallel Algorithms/Architecture Synthesis","volume":"262 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings the First Aizu International Symposium on Parallel Algorithms/Architecture Synthesis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AISPAS.1995.401344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The paper proposes a new architecture, which has the potential to support low-level image processing as well as intermediate and high-level vision analysis efficiently. The integrated architecture consists of a mesh of processors enhanced with an efficient recursive network. Low-level image processing is performed on the mesh processor, while intermediate and high-level vision analysis is performed on the recursive network. The interaction between the two levels is supported by parallel data and control paths. To illustrate the power of such a two-level system, we present efficient parallel algorithms for a variety of problems from low-level image processing to high-level vision. Representative applications include image component labeling convexity problems, and histogram-type operations. Through time complexity analysis, we show that the integrated architecture meets the requirements of many image understanding tasks.<>